System, method and recording medium for antifragile computing problem management

ABSTRACT

A computing problem management method, system, and non-transitory computer readable medium, include detecting an impending problem of a computing system, spawning a plurality of replicas when the detecting detects the impending problem, and launching a plurality of versions of an action, each version being launched and performed on a different replica of the plurality of replicas.

BACKGROUND

The present invention relates generally to a computing problemmanagement method, and more particularly, but not by way of limitation,to a system, method, and recording medium for spawning multiple replicasto pre-emptively fix a computing problem (e.g., an impending systemcrash, etc.).

Conventional techniques to fix computing problems have considered avirtual machine monitor (VMM) in a data processing system which handleserrors involving virtual machines (VMs) in the processing system. Forinstance, the conventional techniques have considered an error managerin the VMM that may detect an uncorrectable error in involving acomponent associated with a first VM in the processing system. Inresponse to detection of that error, the error manager may terminate thefirst VM, while allowing a second VM in the processing system tocontinue operating. In one embodiment, the error manager automaticallydetermines which VM is affected by the uncorrectable error, in responseto detecting the uncorrectable error. The error manager may alsoautomatically spawn a new VM to replace the first VM, if the processingsystem has sufficient resources to support the new VM. Other embodimentsare described and claimed. However, the conventional techniques arelimited by resources for the virtual machines and because of the limitedresources; few VMs can run on the system, which allows the problem(e.g., a system crash) to occur before the VMs can eliminate theproblem.

That is, the inventors have identified a technical problem that becausecomputer systems are extremely complex, there will be errors, faults,mistakes, miscomputations, etc. that inevitably lead to a systemproblem. Even in a well-tested system, errors, faults, mistakes,miscomputations, etc. will occur during a run-time of the computersystem that will lead to a system problem.

SUMMARY

Thus, the inventors have realized a technical solution to the technicalproblem to satisfy a long-felt need in computer systems and to improve acomputer-technology (e.g., to limit (prevent) problems of a computersystem) by spawning multiple replicas in an environment having unlimitedresources (e.g., cloud computing), each replica can run a different typeof action to the problem (e.g., a solution to fix the errors, faults,mistakes, miscomputations, etc.) of the system in real-time to prevent aproblem. And, via learning from a successful replica of the spawnedreplicas, the inventors have realized that the system may becomeso-called “antifragile” such that a replica can be spawned for eacherror in real-time, thereby preventing all system problems by using therealized technical solution.

In an exemplary embodiment, the present invention can provide acomputing problem management method, the method including detecting animpending problem of a computing system, spawning a plurality ofreplicas when the detecting detects the impending problem, and launchinga plurality of versions of an action, each version being launched andperformed on a different replica of the plurality of replicas.

Further, in another exemplary embodiment, the present invention canprovide a non-transitory computer-readable recording medium recording acomputing problem management program, the program causing a computer toperform: detecting an impending problem of a computing system, spawninga plurality of replicas when the detecting detects the impendingproblem, and launching a plurality of versions of an action, eachversion being launched and performed on a different replica of theplurality of replicas.

Even further, in another exemplary embodiment, the present invention canprovide a computing problem management system, said system including aprocessor, and a memory, the memory storing instructions to cause theprocessor to: detect an impending problem of a computing system, spawn aplurality of replicas when the detecting detects the impending problem,and launch a plurality of versions of an action, each version beinglaunched and performed on a different replica of the plurality ofreplicas.

There has thus been outlined, rather broadly, an embodiment of theinvention in order that the detailed description thereof herein may bebetter understood, and in order that the present contribution to the artmay be better appreciated. There are, of course, additional exemplaryembodiments of the invention that will be described below and which willform the subject matter of the claims appended hereto.

It is to be understood that the invention is not limited in itsapplication to the details of construction and to the arrangements ofthe components set forth in the following description or illustrated inthe drawings. The invention is capable of embodiments in addition tothose described and of being practiced and carried out in various ways.Also, it is to be understood that the phraseology and terminologyemployed herein, as well as the abstract, are for the purpose ofdescription and should not be regarded as limiting.

As such, those skilled in the art will appreciate that the conceptionupon which this disclosure is based may readily be utilized as a basisfor the designing of other structures, methods and systems for carryingout the several purposes of the present invention. It is important,therefore, that the claims be regarded as including such equivalentconstructions insofar as they do not depart from the spirit and scope ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The exemplary aspects of the invention will be better understood fromthe following detailed description of the exemplary embodiments of theinvention with reference to the drawings.

FIG. 1 exemplarily shows a high-level flow chart for a computing problemmanagement method 100.

FIG. 2 depicts a cloud-computing node according to an embodiment of thepresent invention.

FIG. 3 depicts a cloud-computing environment according to anotherembodiment of the present invention.

FIG. 4 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The invention will now be described with reference to FIGS. 1-4, inwhich like reference numerals refer to like parts throughout. It isemphasized that, according to common practice, the various features ofthe drawing are not necessarily to scale. On the contrary, thedimensions of the various features can be arbitrarily expanded orreduced for clarity. Exemplary embodiments are provided below forillustration purposes and do not limit the claims.

With reference now to FIG. 1, the computing problem management method100 includes various steps to limit (prevent) an impending computingproblem (e.g., a computer crash) resulting from an impending software orhardware status change, state change, hardware faults, etc. by spawningreplicas that launch actions to prevent the impending computing problem.Moreover, the method (system) can benefit from “learning” from pastfixes to computing problems (e.g., through feedback) to create anantifragile computing system. As shown in at least FIG. 3, one or morecomputers of a computer system 12 can include a memory 28 havinginstructions stored in a storage system to perform the steps of FIG. 1.

With the use of these various steps and instructions, the computingproblem management method 100 may act in a more sophisticated and usefulfashion, and in a cognitive manner while giving the impression of mentalabilities and processes related to knowledge, attention, memory,judgment and evaluation, reasoning, and advanced computation. That is, asystem is said to be “cognitive” if it possesses macro-scaleproperties—perception, goal-oriented behavior, learning/memory andaction—that characterize systems (i.e., humans) that all agree arecognitive.

Cognitive states are defined as functions of measures of a user's totalbehavior collected over some period of time from at least one personalinformation collector (e.g., including musculoskeletal gestures, speechgestures, eye movements, internal physiological changes, measured byimaging circuits, microphones, physiological and kinematic sensors in ahigh dimensional measurement space, etc.) within a lower dimensionalfeature space. In one exemplary embodiment, certain feature extractiontechniques are used for identifying certain cognitive and emotionaltraits. Specifically, the reduction of a set of behavioral measures oversome period of time to a set of feature nodes and vectors, correspondingto the behavioral measures' representations in the lower dimensionalfeature space, is used to identify the emergence of a certain cognitivestate(s) over that period of time. One or more exemplary embodiments usecertain feature extraction techniques for identifying certain cognitivestates. The relationship of one feature node to other similar nodesthrough edges in a graph corresponds to the temporal order oftransitions from one set of measures and the feature nodes and vectorsto another. Some connected subgraphs of the feature nodes are hereinalso defined as a “cognitive state”. The present application alsodescribes the analysis, categorization, and identification of thesecognitive states further feature analysis of subgraphs, includingdimensionality reduction of the subgraphs, for example graphicalanalysis, which extracts topological features and categorizes theresultant subgraph and its associated feature nodes and edges within asubgraph feature space.

Although as shown in FIGS. 2-4 and as described later, the computersystem/server 12 is exemplarily shown in cloud computing node 10 as ageneral-purpose computing circuit which may execute in a layer thecomputing problem management system method (FIG. 3), it is noted thatthe present invention can be implemented outside of the cloudenvironment.

Step 101 detects an impending problem of a computing system (e.g., acomputer, a smart phone, a smart watch, a head-mounted display, a gameconsole, network components (such as a router), etc. The impendingproblem comprises an impending device software or hardware status change(e.g., a fault, system crash, hard-to-recover state, slowness, etc.), adesired state change (e.g., installing a device driver to make a newperipheral work), hardware faults (e.g., writing to memory and disks),etc. It is noted that Step 101 can detect the impending problemautomatically or the impending problem can be suggested by a user whensome aspect of device usage appears to be amiss (e.g., computing systemrunning too slow, response time slow, etc.).

In response to the detection by Step 101, Step 102 spawns a plurality ofreplicas (e.g., Virtual Machines (VMs), containers, etc.) of a system inan environment with an “unlimited” resource (e.g., a cloud environment).A fractal spawning of replicas can be done by Step 102, with childrenreplicas having some relationship of parameters with the parent replica.

Each of the replicas is capable of performing a different action toresolve, limit, and/or prevent the impending computing problem.

Step 103 launches a different action on each of the replicas forpotentially resolving the impending computing problem. That is, variousversions of the actions are automatically launched on the replicas(e.g., each replica performs a different action to resolve the computingproblem). Once the actions are performed by the replicas, the resultingstate of replicas is presented to the user to be chosen or the bestresult can be chosen, automatically.

The actions include, for example, software patches, different version ofpatches, possible solutions to prevent a system crash, and changes intiming of how the actions are spaced. The actions can also include aspread of actions, such as a spread of patches from a narrow range ofdates or a wide range of dates (e.g., a plurality of patches for asystem during a predetermined period of time such that the replicas eachperform all of the actions within the predetermined period of time tofind the one of the patches that can resolve the computing problem).Each action may have an associated with risk, which also has a spread ofvalues (e.g., some actions may delete other files, cause data to belost, etc.).

The various versions of actions can be retrieved from various resourcesof a replica database 130 such as help pages, red books, web community,blogs, etc. The existing text mining techniques can be applied toextract the list of actions (e.g., a “recipe”) from these resources. Thesuccessful recipes can be saved in the replica database 130 for furtheruse. In other words, the actions can be based on past actions of otherreplicas resolving a similar problem.

Step 103 can choose which actions to launch by, for example, geneticalgorithms where random selection is introduced. From a set of known“recipes”, Step 103 can create modified actions by adding extra steps(randomly) or replacing a few steps with other actions chosen “randomly”to find a better cure for the computing problem.

Other kinds of parameter and action variations may be triggeredaccording to various distributions, with an eye toward white noise,Gaussian noise, voting, learning, a controlled spread relating to meanand standard deviation, multidimensional distributions, etc. Forexample, a conservative approach can select actions to launch by Step103 from a set that has a multivariate normal distribution whichincludes spreads along a range of patches, a date of the patch,experimental patches, beta patches, risk levels, etc. Alternatively, awider distribution can be used of tests and trials in a parameter spacefor actions that can resolve the computing problem to help “save” a userfrom an impending crash. Other noise distributions of trial parametersmay be tried, such as white, pink, and brown noise distributions.Parameters tried can relate to different version of patches (e.g., adate distribution), changes in timing of how actions are spaced, etc.

That is, each replica includes a different version of the action ormodified version such that when Step 103 launches the actions on thereplicas, each action realizes a different potential resolution to thecomputing problem. By spawning the plurality of replicas in Step 102based on an “unlimited” resource environment such as a cloud-computingnetwork, the amount of actions that can be performed is greatlyincreased versus the capability of a single system launching replicas.

The number of replicas spawned by Step 102 can be chosen considering acost (money, time, resources, etc.) to launch the replicas and testingout the actions. The number of replicas may also be chosen consideringthe criticality of the resulting state.

Also, a number N (where N is an integer), a location, and a nature ofthe launched actions on the replicas by Steps 102 and Step 103 can becontrolled by a cognitive characteristics of the user 150 (e.g. acurrent distraction level of the user, an ability of a user to handle aparticular nature and number of VMs, etc.) That is, Step 103 chooses theactions automatically, but in other cases, for example, some users maywant to study the actions and be active participants in selecting one ormore actions for the replicas. In these cases, a user may be able tohandle and assess presented replicas based on any of: a user distractionlevel (e.g. as determined by a number of open windows already onscreen), a cohort of a user (e.g., autism, pre-Alzheimer's, child,fatigue level, experience level with a particular application or classof applications, history of use, job title, etc.), a size of screenbeing used (e.g. a large display versus a small display), etc. On theother hand, an advanced user with a particular history may actuallyappreciate seeing more presented replicas, along with a readout of whatthe differences are among the presented replicas. That is, the number ofreplicas spawned by Step 102 can vary according to the cognitive stateof the user.

Step 104 can introduce new computing problems (simulate a computingproblem) by introducing mistakes, faults, attacks, or failures on thereplicas to make the system “antifragile”. In other words, Step 104 cancreate potential computing problems that the actions launched by Step103 can resolve the potential computing problems before the problemoccurs such that when the computing problem occurs in real-time, thereplica database 130 includes the action to resolve the computingproblem in advance. In this manner, the method 100 can create an“antifragile” computing system. It is noted that antifragility refers tosystems that increase in capability, resilience, or robustness as aresult of past mistakes, faults, attacks, or failures.

Further, Step 105 learns the successful actions for a computing problemof the replicas and stores the successful actions in the replicadatabase 130. That is, Step 105 learns a starting state leading to thecomputing problem, the actions taken on the replica, and a success ofthe action can be saved for the future use. Also, a selected resultingstate (e.g., the surviving combination) can be analyzed by Step 105 tolearn the properties to improve future actions. For example, asuccessful action combination might have a certain patch versionapplied, or missed certain software to install. These properties arelearned for the future spawning of resources in the imminence of afailure, and are repeated as ones of the combinations tried.

The success of the resulting state can be measured by user selection orautomatically determined Step 105 decides the successfulness of theresulting state based on the various metrics according to differentrecovering goals. For example, if a driver has not malfunctioned, theinstallation status can be used to judge the successfulness of theresulting state.

That is, while adaptive systems allow for robustness under a variety ofscenarios (e.g., often unknown during system design), adaptive systemsare not necessarily antifragile. In other words, the difference betweenantifragile and adaptive is the difference between a system that isrobust under volatile environments/conditions, and one that is robust inan previously unknown environment. Thus, by Step 104 introducing newcomputing problems and Step 105 learning from the problems, the method100 can create an antifragile system that can never fail because everysolution to every problem can potentially be pre-worked out by thelearning.

In one embodiment, when Step 101 detects the impending computingproblem, the computing system can be slowed down to give additional timeto launch the actions (and the user may be notified of this intentionalslowdown). For example, if an impending problem is detected and thecomputing system is about to crash, the window may turn pink and theinteractions of the user and the system become slightly slower, as Step102 “rushes” to spawn replicas in rapid experiments to launch actions toresolve the impending computing problem. In other words, the processesof the computing system can be slowed such that the actions launched onthe spawned replicas have more time to resolve the impending problembefore the system crash. Or, the timing or nature of the slowing of thecomputing system can be based on the user cognitive state 150 (e.g.,such as user experience level, user cohort, level of distraction, etc.).Or, the timing or nature of the slowing could be profile based dependingon the user or type of computing system.

A success of the action (or versions of the action) is automaticallydetermined if the computing system continues to operate (e.g., does notcrash). Or, the success of the action can be determined by a userconfirming that the impending problem has subsided.

Exemplary Hardware Aspects, Using a Cloud Computing Environment

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client circuits through athin client interface such as a web browser (e.g., web-based e-mail) Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 2, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop circuits, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or circuits, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingcircuits that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage circuits.

As shown in FIG. 2, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing circuit. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externalcircuits 14 such as a keyboard, a pointing circuit, a display 24, etc.;one or more circuits that enable a user to interact with computersystem/server 12; and/or any circuits (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing circuits. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,circuit drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 3, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing circuits used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingcircuit. It is understood that the types of computing circuits 54A-Nshown in FIG. 3 are intended to be illustrative only and that computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized circuit over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 4, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 3) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 4 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage circuits 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, more particularly relative to thepresent invention, the anti-counterfeiting system 100 and theanti-counterfeiting system 600 described herein.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Further, Applicant's intent is to encompass the equivalents of all claimelements, and no amendment to any claim of the present applicationshould be construed as a disclaimer of any interest in or right to anequivalent of any element or feature of the amended claim.

What is claimed is:
 1. A computing problem management method, the methodcomprising: detecting an impending problem of a computing system;spawning a plurality of replicas when the detecting detects theimpending problem; and launching a plurality of versions of an action,each version being launched and performed on a different replica of theplurality of replicas.
 2. The method of claim 1, wherein each replicaincluding a version of the action is presented to a user for selectionby the user as to which version to perform.
 3. The method of claim 1,further comprising: introducing a plurality of new impending problems onthe plurality of replicas for the launching to launch versions of theaction to resolve; and learning a version that resolves each of theplurality of new impending problems.
 4. The method of claim 1, furthercomprising: introducing a plurality of new impending problems on theplurality of replicas for the launching to launch versions of the actionto resolve; and learning a version that resolves each of the pluralityof new impending problems to thereby create an antifragile computingsystem.
 5. The method of claim 1, wherein the action comprises any oneof: a software patch; different versions of the software patch; a knownsolution to the impending problem; a potential solution to the impendingproblem; and a change in a time of implantation of the action.
 6. Themethod of claim 1, wherein the launching decides the plurality ofversions of the action to launch based on any of: a genetic algorithmincluding a random selection; a white noise; a Gaussian noise; a voting;a controlled spread relating to a mean and a standard deviation; and amultidimensional distribution.
 7. The method of claim 1, wherein anumber of the plurality of replicas is based on at least one of: a costassociated with a resource required for the number of the plurality ofreplicas; and a criticality of resolving the impending problem.
 8. Themethod of claim 1, further comprising learning successful versions ofthe action based on a prior starting state, the version of the action,and a result of the version of the action.
 9. The method of claim 1,wherein a number, a location on the computing system, and a type of theversions of the action of the plurality of replicas is based on acognitive state of a user.
 10. The method of claim 1, wherein, if thedetecting detects the impending problem, a processing speed of thecomputing system is decreased.
 11. The method of claim 1, wherein, ifthe detecting detects the impending problem, a processing speed of thecomputing system is decreased such that the plurality of versions of theaction are performed by the plurality of replicas at a rate faster thana rate of propagation of the impending problem in the computing system.12. The method of claim 1, wherein the spawning spawns the plurality ofreplicas in a cloud-computing system.
 13. The method of claim 1, whereinthe spawning spawns the plurality of replicas in an environment with anunlimited resource.
 14. The method of claim 1, wherein the spawningspawns a plurality of children replicas having a relationship to areplica of the plurality of replicas, and wherein each of the pluralityof children replicas includes a modified action of the replica with therelationship to the plurality of children replicas.
 15. The method ofclaim 1, wherein the impending problem comprises a system crash.
 16. Themethod of claim 1, wherein the impending problem comprises any of: animpending device software or hardware status change including a fault, asystem crash, a hard-to-recover state, and a speed of the impendingdevice; a state change; and a hardware fault.
 17. The method of claim 1,wherein the computing system comprises any of: a computer; a smartphone; a smart watch; a head-mounted display; a game console; and anetwork component.
 18. The method of claim 1, wherein a success of theaction is automatically determined if the computing system continues tooperate.
 19. A non-transitory computer-readable recording mediumrecording a computing problem management program, the program causing acomputer to perform: detecting an impending problem of a computingsystem; spawning a plurality of replicas when the detecting detects theimpending problem; and launching a plurality of versions of an action,each version being launched and performed on a different replica of theplurality of replicas.
 20. A computing problem management system, saidsystem comprising: a processor; and a memory, the memory storinginstructions to cause the processor to: detect an impending problem of acomputing system; spawn a plurality of replicas when the detectingdetects the impending problem; and launch a plurality of versions of anaction, each version being launched and performed on a different replicaof the plurality of replicas.